# Probabilistic Simulation of Aircraft Descent via a Physics-Informed Machine Learning Approach

arXiv: https://arxiv.org/abs/2504.02529 · HTML: https://mlsanity.com/paper/2504.02529/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace](https://mlsanity.com/paper/2601.03120/) (2026)
- [A Future Capabilities Agent for Tactical Air Traffic Control](https://mlsanity.com/paper/2601.04285/) (2026)
- [Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions](https://mlsanity.com/paper/2605.23696/) (2026)

## Abstract (arXiv)

This paper presents a method for generating probabilistic descent trajectories in simulations of real-world airspace. A dataset of 116,066 trajectories harvested from Mode S radar returns in UK airspace was used to train and test the model. Thirteen aircraft types with varying performance characteristics were investigated. It was found that the error in the mean prediction of time to reach the bottom of descent for the proposed method was less than that of the the Base of Aircraft Data (BADA) model by a factor of 10. Furthermore, the method was capable of generating a range of trajectories that were similar to the held out test dataset when analysed in distribution. The proposed method is hybrid, with aircraft drag and calibrated airspeed functions generated probabilistically to parameterise the BADA equations, ensuring the physical plausibility of generated trajectories.